Senior Data Platform Engineer (m/f/d) Berlin, Germany at EIDU GmbH

Berlin, , Germany -

Full Time

Start Date

Immediate

Expiry Date

07 Sep, 26

Salary

87500.0

Posted On

09 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Platform Engineering, AI Integration, Data Governance, AWS, Infrastructure as Code, CI/CD, Kafka, Redshift, dbt Core, Metabase, SQL, Data Modeling, Observability, RBAC, Data Contracts, ETL/ELT

Industry

E-Learning Providers

Description

About Us More than 600 million children worldwide fail to achieve foundational skills like literacy and basic numeracy, despite the majority of them attending school. EIDU is a nonprofit education implementer currently serving nearly a million students in Kenya, with plans to scale to tens of millions through government partnerships. A year-long randomised control trial showed that programs powered by our technology add the equivalent of at least 0.5 years of learning development. You will work in the Berlin-based technology company building the modular enterprise software platform that underpins EIDU's programs and will serve multiple governments across countries to make large-scale education delivery possible. If you'd like to learn more, visit eidu.com About the role We build the learning platform behind education programmes reaching children across Kenya and Nigeria. Every lesson a child takes generates a signal. Our data platform turns that signal into the evidence that tells us, our funders and the governments we report to, whether children are actually learning to read. So when a number is wrong, a decision about a child's education is wrong. Getting that right, at scale, on a platform you can trust, is the job. We're hiring a dedicated Senior Data Platform Engineer to own and rebuild that platform with AI woven through it where AI earns its place, and kept off it where it doesn't. What you will do: Own the data platform end-to-end: Ingestion, warehouse, transformation tooling, BI, scheduling, observability, governance. Today that's Kafka → Redshift, dbt Core, GitHub Actions, and Metabase. Where it goes next is yours to argue for. Build the governed layer that makes AI-assisted analytics trustworthy: Semantic and metric definitions, data contracts, tests. Make sure that numbers that reach a funder or a government programme are reproducible and audited. Put AI to work on the platform itself: Freshness, cost and anomaly detection, pipeline triage, automated first-pass resolution. Automate repeatable and verifiable tasks. Build observability, governance and access control across the stack: scoped credentials, RBAC and access tiers in the warehouse and Metabase, audit, lineage, compliance. Partner with product engineering on product event data: Making it first-class without breaking the warehouse, establish data contracts at that boundary. Collaborate on infrastructure to fuel in-app analytics products. What you should bring: This is right for you if You have opinions about where AI belongs in a data platform and where it categorically doesn't and you can defend the line. You can dissect dbt's tradeoffs and say when the answer is a semantic layer (MetricFlow, Cube, Malloy), SQLMesh, or something we haven't tried yet You have broad command of the modern data stack. Stream and batch, OLAP, orchestration, transformation, observability. You care about the tradeoffs between them. You're fluent enough in AWS, IaC, and CI/CD to ship and run your own tooling without waiting on anyone. (Our platform team owns the underlying infrastructure — k8s, SSO, networking — so it won’t be your day-to-day job.) You're comfortable being a team of one. You'll be the only data engineer on a four-person data team, owning the platform end to end. This probably isn't for you if dbt and the warehouse are the edges of your world. You want a big data team, peers to pair with, or a clearly bounded specialist lane. This is a generalist role where you will be collaborating with other roles. You want a finished platform to optimise rather than one to rebuild and have opinions about. Our Stack An event-sourced microservice backend, with Kafka as the source of truth. Events are sunk and flattened into Redshift. dbt Core transforms raw events into datasets, scheduled through GitHub Actions. Data is exposed through Metabase (open source). What we offer: Work on one of the biggest problems in the world: ensuring every child gets the education they deserve. A small, high-impact team where your work shapes our direction. Annual up-skilling budget Competitive equity-awarding model High degree of flexibility with regard to working hours and vacations A hybrid working arrangement, based in Berlin Compensation between €70,000 and €87,500 (if leveled as Senior) About us

Responsibilities

Own and rebuild the end-to-end data platform, including ingestion, warehousing, and BI, while integrating AI for platform optimization. Establish governed data layers and contracts to ensure reproducible and audited analytics for funders and governments.